We experimented with improving the latency of GNN-based models by using graph machine learning libraries such as DGL [1]}.
DGL requires graphs to be prepared as sparse library-specific data objects, which significantly boosts the inference speed of GNNs.
However, using DGL had a negative impact on the speed of the rest of our pipeline (batched data preparation, decoders, beam search).
This issue is especially amplified for reinforcement learning, where we constantly generate new random datasets at each epoch.
For now, we present timings and results with pure PyTorch code.
We confirm that results are consistent with using DGL, but decided against it in order to run a large volume of experiments for more comprehensive analysis.
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